Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions

Abstract

Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an ‘MPM interactome’ with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities.

Interactome of SARS-CoV-2 Modulated Host Proteins With Computationally Predicted PPIs: Insights From Translational Systems Biology Studies

“…com) (Kupershmidt et al, 2010;Chattopadhyay and Ganapathiraju, 2017). This data analysis platform was used because it allows users to study the effect of diseases and/or drugs on thousands of pre-processed publicly available gene expression datasets and has helped to identify drug candidates for diseases such as schizophrenia (Karunakaran et al, 2019b) (currently undergoing clinical trials (Vishwajit Nimgaonkar, 2022;Vishwajit Nimgaonkar, 2024)) and mesothelioma (Karunakaran et al, 2021) in the past. We compiled a list of 933 chemical compounds whose differential gene expression profiles (drug versus no drug) were negatively correlated with at least one of the four SARS differential gene expression datasets (infected versus non-infected); the 4 SARS datasets we studied were: Calu-3 epithelial cells infected for 48 h with SARS-CoV versus mock infected cells (GSE17400), Calu-3 lung cells infected for 72 h with SARS-CoV Urbani versus mock infected cells (GSE37827), lung fibroblast MRC5 cells 24 h post SARS-CoV infection (high MOI) versus mock infection (GSE56189) and PBMCs from SARS patients versus healthy subjects [GSE1739 (Reghunathan et al, 2005)].…”

“…The threshold of HiPPIP to classify a protein-pair as "a PPI" was set high in such a way that it yields very high-precision predictions even if low recall. Seventeen of the predicted PPIs were tested experimentally and were shown to be true PPIs, namely, 8 PPIs validated by co-immunoprecipitation: DDX58-OASL (Zhu et al, 2014), HMGB1-FLT1 (Ganapathiraju et al, 2016a), HMGB1-KL (Ganapathiraju et al, 2016a), STT3A-RPS25 (Ganapathiraju et al, 2016a), STT3A-SYCP3 (Ganapathiraju et al, 2016a), STT3A-MCAM (Ganapathiraju et al, 2016a), PDCD1-<hidden> (unpublished validation), YWHAE1-<hidden> (unpublished validation), five PPIs validated by in vitro pulldown and mass spectrometry: ALB-KDR (Karunakaran et al, 2021), ALB-PDGFRA (Karunakaran et al, 2021), BAP1-PARP3 (Karunakaran et al, 2021), CLPS-CUTA (Karunakaran et al, 2021), HMGB1-CUTA (Karunakaran et al, 2021) and 4 PPIs validated by co-localization: STX3-LPXN (Ganapathiraju et al, 2016a), STX4-MAPK3 (Ganapathiraju et al, 2016a), IFT88-KL (unpublished validation) and WDR5-IGFBP3 (unpublished validation). Some of the predicted PPIs proved to have high translational impact.…”

Multiple institutions’ research findings using the National Mesothelioma Virtual Bank

“…Moreover, for malignant pleural mesothelioma, we developed a novel computational algorithm that automatically discovered 364 potential protein-protein interactions, of which five interactions (BAP1-PARP3, KDR-ALB, PDGFRA- ALB, CUTA-HMGB1, and CUTA-CLPS) were validated experimentally; our comparative transcriptiome analysis identified five potentially repurposable drugs targeting the interactome proteins (cabazitaxel, primaquine, pyrimethamine, trimethroprim, and gliclazide). 28 To share our findings with the whole research community, we make the discovered interactions publicly available through Wiki-MPM (https://hagrid.dbmi.pitt.edu/wiki-MPM/, last accessed on July 31 st , 2022). Realizing the computational approach may be more needed by rare types, we further applied similar techniques on malignant peritoneal mesothelioma and identified 417 novel protein-protein interactions.…”

Novel Protein-Protein Interactions Highlighting the Crosstalk between Hypoplastic Left-Heart Syndrome, Ciliopathies and Neurodevelopmental Delays

“…HiPPIP computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene and gene expression in microarray experiments, and classifies the pairwise features as interacting or noninteracting based on a random forest model [22]. This method has been validated as accurate by computational evaluations [22] and experimental validations [22,27,28]. The novel PPIs predicted using HiPPIP have yielded discoveries with translational impact, including identifying the central role of cilia in CHD [12,22,29].…”